Abstract：
The mechanisms underlying drug addiction remain nebulous. Furthermore, new psychoactive substances (NPS) are being developed to circumvent legal control; hence, rapid NPS identification is urgently needed. Here, we present the construction of the comprehensive database of controlled substances, AddictedChem. This database integrates the following information on controlled substances from the US Drug Enforcement Administration: physical and chemical characteristics; classified literature by Medical Subject Headings terms and target binding data; absorption, distribution, metabolism, excretion, and toxicity; and related genes, pathways, and bioassays. We created 29 predictive models for NPS identification using five machine learning algorithms and seven molecular descriptors. The best performing models achieved a balanced accuracy (BA) of 0.940 with an area under the curve (AUC) of 0.986 for the test set and a BA of 0.919 and an AUC of 0.968 for the external validation set, which were subsequently used to identify potential NPS with a consensus strategy. Concurrently, a chemical space that included the properties of vectorised addictive compounds was constructed and integrated with AddictedChem, illustrating the principle of diversely existing NPS from a macro perspective. Based on these potential applications, AddictedChem could be considered a highly promising tool for NPS identification and evaluation.
摘要：
药物成瘾的潜在机制仍然模糊。此外，正在开发新的精神活性物质(NPS)来规避法律控制；因此，迫切需要快速识别NPS。这里,我们介绍了受控物质综合数据库的建设，上瘾的Chem.该数据库整合了美国药物管制局关于受控物质的以下信息：物理和化学特征；按医学主题词术语和目标结合数据分类的文献；吸收，分布，新陈代谢，排泄,和毒性；以及相关基因，通路,和生物测定。我们使用五种机器学习算法和七个分子描述符创建了29种用于NPS识别的预测模型。性能最好的模型实现了0.940的平衡精度（BA），测试集的曲线下面积（AUC）为0.986，外部验证集的BA为0.919，AUC为0.968。随后使用共识策略识别潜在的NPS。同时,构建了一个包含矢量化成瘾化合物特性的化学空间，并将其与成瘾化学整合在一起，从宏观角度说明了多样化存在的NPS的原理。基于这些潜在的应用,成瘾化学可以被认为是用于NPS识别和评估的非常有前途的工具。
